> My first stop is usually wikipedia:
[...]
Thanks.
So I I'known that I have to call the beast a
"empirical inverse survival function", Robert would
also have foundit easier to help.
Anyway, step by step...
> In the case of the weight of pigs, it would be to cumulative weight of
> all pigs with a weight less than the given bin boundary weight.
> If values were income, then it would be the aggregated income of all
> individual with an income below the bin bin boundary.
> So it makes sense, given this is what you want (below).
Exactly!
Or for precipitation:
a) count: number of precipitation events that
ocurred up to a certain limit
b) sum: precipitation total registered up to that limit
> there might be a mistake in the treatment of a cell when
> reversing, when I run your example the highest value is
> not equal to values.sum()
This has made me think again. Small point.
See here:
ecdf_sums = np.hstack([0.0, sums[0].cumsum() ])
ecdf_sums = np.hstack([sums[0].cumsum() ])
I had to adjust the classes in the spreadsheet by
replacing the first class limit by 0.0.
I had modifed this yesterday to a different value
(0.265152) as I was testing the code.
from:
0.265152, 0.487273, 0.709394, 0.931515,
1.153636, 1.375758, 1.597879, 1.820000,
2.042121, 2.264242, 2.486364
to:
0.0, 0.487273, 0.709394, 0.931515,
1.153636, 1.375758, 1.597879, 1.820000,
2.042121, 2.264242, 2.486364
Now everything is fine. Results and curves match.
> But I'm not sure yet, what's going on.
1) first I didn't know how to develop the code for a
"empirical inverse survival function" in numpy
2) I screwed my spreadsheet classes up while
testing and verifying my numpy code.
Again, would a function for the
"empirical inverse survival function" qualify for the
inclusion into numpy or scipy?
Thanks for the help.
Best regards,
Timmie